scholarly journals Supplier Selection Study under the Respective of Low-Carbon Supply Chain: A Hybrid Evaluation Model Based on FA-DEA-AHP

2018 ◽  
Vol 10 (3) ◽  
pp. 564 ◽  
Author(s):  
Xiangshuo He ◽  
Jian Zhang
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Evaluation Model ◽  
Low Carbon ◽  
Model Based ◽  
Carbon Supply

Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain

2012 ◽  
Vol 39 (9) ◽  
pp. 8182-8192 ◽  
Author(s):  
Krishnendu Shaw ◽  
Ravi Shankar ◽  
Surendra S. Yadav ◽  
Lakshman S. Thakur
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Supplier Selection ◽  
Fuzzy Ahp ◽  
Low Carbon ◽  
Multi Objective ◽  
Carbon Supply

scholarly journals A Fuzzy-Grey Multicriteria Decision Making Approach for Green Supplier Selection in Low-Carbon Supply Chain

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qinghua Pang ◽  
Tiantian Yang ◽  
Mingzhen Li ◽  
Yi Shen
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Multicriteria Decision Making ◽  
Low Carbon ◽  
Chain Management ◽  
Multicriteria Decision ◽  
Green Supplier Selection ◽  
Carbon Supply

Due to the increasing awareness of global warming and environmental protection, many practitioners and researchers have paid much attention to the low-carbon supply chain management in recent years. Green supplier selection is one of the most critical activities in the low-carbon supply chain management, so it is important to establish the comprehensive criteria and develop a method for green supplier selection in low-carbon supply chain. The paper proposes a fuzz-grey multicriteria decision making approach to deal with these problems. First, the paper establishes 4 main criteria and 22 subcriteria for green supplier selection. Then, a method integrating fuzzy set theory and grey relational analysis is proposed. It uses the membership function of normal distribution to compare each supplier and uses grey relation analysis to calculate the weight of each criterion and improves fuzzy comprehensive evaluation. The proposed method can make the localization of individual green supplier more objectively and more accurately in the same trade. Finally, a case study in the steel industry is presented to demonstrate the effectiveness of the proposed approach.

Novel q-rung orthopair fuzzy interaction aggregation operators and their application to low-carbon green supply chain management

2021 ◽  
pp. 1-18
Author(s):  
Muhammad Riaz ◽  
Harish Garg ◽  
Hafiz Muhammad Athar Farid ◽  
Muhammad Aslam
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Weighted Averaging ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Environmental Concerns ◽  
Low Carbon ◽  
Chain Management ◽  
Carbon Supply

The low-carbon supply chain management is big a challenge for the researchers due to the rapid increase in global warming and environmental concerns. With the advancement of the environmental concerns and social economy, it is an unavoidable choice for a business to achieve sustainable growth for low-carbon supply chain management. Since the root of the chain depends upon the supplier selection and choosing an excellent low-carbon supply. Green supplier selection is one of the most crucial activities in low-carbon supply chain management, it is critical to develop rigorous requirements and a system for selection in low-carbon green supply chain management (LCGSCM). A q-rung orthopair fuzzy number (q-ROFN) is pair of membership degree (MD) and non-membership degrees (NMD) which is reliable to address uncertainties in the various real-life problems. This article sets out a decision analysis approach for interactions between MDs and NMDs with the help of q-ROFNs. For this objective, we develop new aggregation operators (AOs) named as, q-rung orthopair fuzzy interaction weighted averaging (q-ROFIWA) operator, q-rung orthopair fuzzy interaction ordered weighted averaging (q-ROFIOWA) operator, q-rung orthopair fuzzy interaction hybrid averaging (q-ROFIHA) operator, q-rung orthopair fuzzy interaction weighted geometric (q-ROFIWG) operator, q-rung orthopair fuzzy interaction ordered weighted geometric (q-ROFIOWG) operator and q-rung orthopair fuzzy interaction hybrid geometric (q-ROFIHG) operator. These AOs define an advanced approach for information fusion and modeling uncertainties in multi-criteria decision-making (MCDM). At the end, a robust MCDM approach based on newly developed AOs is developed. Some significant properties of these AOS are analyzed and the efficiency of the developed approach is assessed with a practical application towards sustainable low-carbon green supply chain management.

scholarly journals Achieve a low carbon supply chain through product mix

2017 ◽  
Vol 117 (10) ◽  
pp. 2468-2484 ◽  
Author(s):  
Xu Chen ◽  
Xiaojun Wang
Keyword(s):  
Supply Chain ◽  
Trade Policy ◽  
Carbon Emissions ◽  
Channel Structure ◽  
Low Carbon ◽  
Cap And Trade ◽  
Product Mix ◽  
Content Type ◽  
Carbon Supply ◽  
Trade Offs

Purpose In the era of climate change, industrial organizations are under increasing pressure from consumers and regulators to reduce greenhouse gas emissions. The purpose of this paper is to examine the effectiveness of product mix as a strategy to deliver the low carbon supply chain under the cap-and-trade policy. Design/methodology/approach The authors incorporate the cap-and-trade policy into the green product mix decision models by using game-theoretic approach and compare these decisions in a decentralized model and a centralized model, respectively. The research explores potential behavioral changes under the cap-and-trade in the context of a two-echelon supply chain. Findings The analysis results show that the channel structure has significant impact on both economic and environmental performances. An integrated supply chain generates more profits. In contrast, a decentralized supply chain has lower carbon emissions. The cap-and-trade policy makes a different impact on the economic and environmental performances of the supply chain. Balancing the trade-offs is critical to ensure the long-term sustainability. Originality/value The research offers many interesting observations with respect to the effect of product mix strategy on operational decisions and the trade-offs between costs and carbon emissions under the cap-and-trade policy. The insights derived from the analysis not only help firms to make important operational and strategic decisions to reduce carbon emissions while maintaining their economic competitiveness, but also make meaningful contribution to governments’ policy making for carbon emissions control.

Measuring Low Carbon Supply Chain

2019 ◽  
pp. 1105-1116
Author(s):  
Muhammad Shabir Shaharudin ◽  
Yudi Fernando
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Greenhouse Gas Emission ◽  
Review Literature ◽  
Extended Model ◽  
Low Carbon ◽  
Chain Management ◽  
Carbon Supply ◽  
Or Practice ◽  
Reduce Carbon Emission

The threat of climate change is due to increasing carbon emissions of manufacturing production and transportation. Currently, government is encouraging manufacturing to reduce carbon emission and conduct low carbon supply chain management (LCSCM). In order to solve the greenhouse gas emission dilemma, LCSCM is essential for manufacturing firms' stakeholders. Supply chain partners are expected to know the proper measurement of emissions to solve this problem. This chapter's aim is to review literature on how to measure LCSCM. In the past, the concept of green supply chain management (GSCM) was practiced to promote and reduce environmental risks. However, GSCM is a driver or practice to achieve environmental outcomes. The extended model of GSCM currently practices LCSCM through carbon footprint (CF) concept. In other words, LCSCM is an outcome that both interests researchers and persuades practitioners.

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